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logs.log
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2021-03-28 14:21:03,118:INFO:PyCaret Supervised Module
2021-03-28 14:21:03,119:INFO:ML Usecase: classification
2021-03-28 14:21:03,119:INFO:version 2.3.0
2021-03-28 14:21:03,119:INFO:Initializing setup()
2021-03-28 14:21:03,119:INFO:setup(display=None, profile_kwargs=None, profile=False, verbose=False, silent=True, log_data=False, log_profile=False, log_plots=False, experiment_name=None, log_experiment=False, session_id=None, html=True, custom_pipeline=None, use_gpu=False, n_jobs=-1, fold_groups=None, fold_shuffle=False, fold=10, fold_strategy=kfold, data_split_stratify=False, data_split_shuffle=True, transform_target_method=box-cox, transform_target=False, fix_imbalance_method=None, fix_imbalance=False, interaction_threshold=0.01, feature_ratio=False, feature_interaction=False, feature_selection_method=classic, feature_selection_threshold=0.8, feature_selection=False, group_names=None, group_features=None, polynomial_threshold=0.1, trigonometry_features=False, polynomial_degree=2, polynomial_features=False, cluster_iter=20, create_clusters=False, remove_perfect_collinearity=True, multicollinearity_threshold=0.9, remove_multicollinearity=False, outliers_threshold=0.05, remove_outliers=False, bin_numeric_features=None, rare_level_threshold=0.1, combine_rare_levels=False, ignore_low_variance=False, pca_components=None, pca_method=linear, pca=False, unknown_categorical_method=least_frequent, handle_unknown_categorical=True, transformation_method=yeo-johnson, transformation=False, normalize_method=zscore, normalize=True, ignore_features=['EmployeeNumber', 'StandardHours', 'EmployeeCount', 'Over18'], date_features=None, numeric_iterative_imputer=lightgbm, numeric_imputation=mean, numeric_features=['DistanceFromHome', 'HourlyRate', 'DailyRate', 'MonthlyIncome', 'MonthlyRate', 'NumCompaniesWorked', 'PercentSalaryHike', 'TotalWorkingYears', 'YearsAtCompany', 'YearsInCurrentRole', 'YearsWithCurrManager', 'TrainingTimesLastYear', 'YearsSinceLastPromotion'], high_cardinality_method=frequency, high_cardinality_features=None, ordinal_features={'StockOptionLevel': ['0', '1', '2', '3'], 'EnvironmentSatisfaction': ['1', '2', '3', '4'], 'JobInvolvement': ['1', '2', '3', '4'], 'JobSatisfaction': ['1', '2', '3', '4'], 'Education': ['1', '2', '3', '4', '5'], 'PerformanceRating': ['3', '4'], 'RelationshipSatisfaction': ['1', '2', '3', '4'], 'WorkLifeBalance': ['1', '2', '3', '4']}, categorical_iterative_imputer=lightgbm, categorical_imputation=constant, categorical_features=['BusinessTravel', 'Department', 'EducationField', 'JobRole', 'Gender', 'JobLevel', 'JobRole', 'MaritalStatus', 'OverTime', 'WorkLifeBalance'], iterative_imputation_iters=5, imputation_type=simple, preprocess=True, test_data= Age BusinessTravel DailyRate Department \
1041 28 Travel_Rarely 866 Sales
184 53 Travel_Rarely 1084 Research & Development
1222 24 Travel_Rarely 240 Human Resources
67 45 Travel_Rarely 1339 Research & Development
220 36 Travel_Rarely 1396 Research & Development
... ... ... ... ...
1009 58 Travel_Rarely 1055 Research & Development
757 34 Travel_Rarely 216 Sales
1361 26 Travel_Frequently 1096 Research & Development
376 51 Travel_Rarely 1178 Sales
314 39 Travel_Rarely 117 Research & Development
DistanceFromHome Education EducationField EmployeeCount \
1041 5 3 Medical 1
184 13 2 Medical 1
1222 22 1 Human Resources 1
67 7 3 Life Sciences 1
220 5 2 Life Sciences 1
... ... ... ... ...
1009 1 3 Medical 1
757 1 4 Marketing 1
1361 6 3 Other 1
376 14 2 Life Sciences 1
314 10 1 Medical 1
EmployeeNumber EnvironmentSatisfaction Gender HourlyRate \
1041 1469 4 Male 84
184 250 4 Female 57
1222 1714 4 Male 58
67 86 2 Male 59
220 304 4 Male 62
... ... ... ... ...
1009 1423 4 Female 76
757 1047 2 Male 75
1361 1918 3 Male 61
376 500 3 Female 87
314 429 3 Male 99
JobInvolvement JobLevel JobRole JobSatisfaction \
1041 3 2 Sales Executive 1
184 4 2 Manufacturing Director 1
1222 1 1 Human Resources 3
67 3 3 Research Scientist 1
220 3 2 Laboratory Technician 2
... ... ... ... ...
1009 3 5 Research Director 1
757 4 2 Sales Executive 4
1361 4 1 Laboratory Technician 4
376 3 2 Sales Executive 4
314 3 4 Manager 1
MaritalStatus MonthlyIncome MonthlyRate NumCompaniesWorked Over18 \
1041 Single 8463 23490 0 Y
184 Divorced 4450 26250 1 Y
1222 Married 1555 11585 1 Y
67 Divorced 9724 18787 2 Y
220 Single 5914 9945 8 Y
... ... ... ... ... ...
1009 Married 19701 22456 3 Y
757 Divorced 9725 12278 0 Y
1361 Married 2544 7102 0 Y
376 Married 4936 14862 4 Y
314 Married 17068 5355 1 Y
OverTime PercentSalaryHike PerformanceRating RelationshipSatisfaction \
1041 No 18 3 4
184 No 11 3 3
1222 No 11 3 3
67 No 17 3 3
220 No 16 3 4
... ... ... ... ...
1009 Yes 21 4 3
757 No 11 3 4
1361 No 18 3 1
376 No 11 3 3
314 Yes 14 3 4
StandardHours StockOptionLevel TotalWorkingYears \
1041 80 0 6
184 80 2 5
1222 80 1 1
67 80 1 25
220 80 0 16
... ... ... ...
1009 80 1 32
757 80 1 16
1361 80 1 8
376 80 1 18
314 80 0 21
TrainingTimesLastYear WorkLifeBalance YearsAtCompany \
1041 4 3 5
184 3 3 4
1222 2 3 1
67 2 3 1
220 3 4 13
... ... ... ...
1009 3 3 9
757 2 2 15
1361 3 3 7
376 2 2 7
314 3 3 21
YearsInCurrentRole YearsSinceLastPromotion YearsWithCurrManager \
1041 4 1 3
184 2 1 3
1222 0 0 0
67 0 0 0
220 11 3 7
... ... ... ...
1009 8 1 5
757 1 0 9
1361 7 7 7
376 7 0 7
314 9 11 10
Attrition
1041 No
184 No
1222 Yes
67 No
220 No
... ...
1009 No
757 No
1361 No
376 No
314 No
[588 rows x 35 columns], train_size=0.7, available_plots={'parameter': 'Hyperparameters', 'auc': 'AUC', 'confusion_matrix': 'Confusion Matrix', 'threshold': 'Threshold', 'pr': 'Precision Recall', 'error': 'Prediction Error', 'class_report': 'Class Report', 'rfe': 'Feature Selection', 'learning': 'Learning Curve', 'manifold': 'Manifold Learning', 'calibration': 'Calibration Curve', 'vc': 'Validation Curve', 'dimension': 'Dimensions', 'feature': 'Feature Importance', 'feature_all': 'Feature Importance (All)', 'boundary': 'Decision Boundary', 'lift': 'Lift Chart', 'gain': 'Gain Chart', 'tree': 'Decision Tree'}, ml_usecase=classification, target=Attrition)
2021-03-28 14:21:03,119:INFO:Checking environment
2021-03-28 14:21:03,119:INFO:python_version: 3.6.13
2021-03-28 14:21:03,119:INFO:python_build: ('default', 'Feb 23 2021 21:15:04')
2021-03-28 14:21:03,119:INFO:machine: x86_64
2021-03-28 14:21:03,146:INFO:platform: Linux-4.14.214-160.339.amzn2.x86_64-x86_64-with-glibc2.9
2021-03-28 14:21:03,147:WARNING:cannot find psutil installation. memory not traceable. Install psutil using pip to enable memory logging.
2021-03-28 14:21:03,147:INFO:Checking libraries
2021-03-28 14:21:03,147:INFO:pd==1.1.4
2021-03-28 14:21:03,147:INFO:numpy==1.19.5
2021-03-28 14:21:03,147:INFO:sklearn==0.23.2
2021-03-28 14:21:03,147:INFO:lightgbm==3.2.0
2021-03-28 14:21:03,147:WARNING:catboost not found
2021-03-28 14:21:03,147:WARNING:xgboost not found
2021-03-28 14:21:03,482:INFO:mlflow==1.15.0
2021-03-28 14:21:03,482:INFO:Checking Exceptions
2021-03-28 14:21:03,484:INFO:Declaring global variables
2021-03-28 14:21:03,485:INFO:USI: f887
2021-03-28 14:21:03,485:INFO:pycaret_globals: {'_gpu_n_jobs_param', 'transform_target_param', 'seed', 'logging_param', '_all_models', 'y_train', 'gpu_param', 'pycaret_globals', 'experiment__', 'X_test', 'y', '_all_models_internal', 'data_before_preprocess', 'X_train', 'master_model_container', '_all_metrics', '_available_plots', 'log_plots_param', '_ml_usecase', 'exp_name_log', '_internal_pipeline', 'imputation_classifier', 'transform_target_method_param', 'html_param', 'fold_param', 'display_container', 'fix_imbalance_method_param', 'X', 'fold_shuffle_param', 'fix_imbalance_param', 'y_test', 'imputation_regressor', 'stratify_param', 'fold_generator', 'target_param', 'prep_pipe', 'USI', 'create_model_container', 'fold_groups_param', 'iterative_imputation_iters_param', 'n_jobs_param'}
2021-03-28 14:21:03,485:INFO:Preparing display monitor
2021-03-28 14:21:03,485:INFO:Importing libraries
2021-03-28 14:21:03,485:INFO:Copying data for preprocessing
2021-03-28 14:21:03,485:INFO:Declaring preprocessing parameters
2021-03-28 14:21:03,486:INFO:Creating preprocessing pipeline
2021-03-28 14:21:03,514:INFO:Preprocessing pipeline created successfully
2021-03-28 14:21:03,514:ERROR:(Process Exit): setup has been interupted with user command 'quit'. setup must rerun.
2021-03-28 14:21:03,514:INFO:Creating global containers
2021-03-28 14:21:03,515:INFO:Internal pipeline: Pipeline(memory=None, steps=[('empty_step', 'passthrough')], verbose=False)
2021-03-28 14:21:04,170:WARNING:Couldn't import xgboost.XGBClassifier
2021-03-28 14:21:04,170:WARNING:Couldn't import catboost.CatBoostClassifier
2021-03-28 14:21:04,300:WARNING:Couldn't import xgboost.XGBClassifier
2021-03-28 14:21:04,301:WARNING:Couldn't import catboost.CatBoostClassifier
2021-03-28 14:21:04,301:INFO:Creating grid variables
2021-03-28 14:21:04,305:INFO:create_model_container: 0
2021-03-28 14:21:04,305:INFO:master_model_container: 0
2021-03-28 14:21:04,305:INFO:display_container: 1
2021-03-28 14:21:04,315:INFO:Pipeline(memory=None,
steps=[('dtypes',
DataTypes_Auto_infer(categorical_features=['BusinessTravel',
'Department',
'EducationField',
'JobRole', 'Gender',
'JobLevel',
'JobRole',
'MaritalStatus',
'OverTime',
'WorkLifeBalance'],
display_types=False,
features_todrop=['EmployeeNumber',
'StandardHours',
'EmployeeCount',
'Over18'],
id_columns=[],
ml_usecase='classification...
('P_transform', 'passthrough'), ('binn', 'passthrough'),
('rem_outliers', 'passthrough'), ('cluster_all', 'passthrough'),
('dummy', Dummify(target='Attrition')),
('fix_perfect', Remove_100(target='Attrition')),
('clean_names', Clean_Colum_Names()),
('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),
('dfs', 'passthrough'), ('pca', 'passthrough')],
verbose=False)
2021-03-28 14:21:04,315:INFO:setup() succesfully completed......................................
2021-03-28 14:27:59,517:INFO:PyCaret Supervised Module
2021-03-28 14:27:59,517:INFO:ML Usecase: classification
2021-03-28 14:27:59,517:INFO:version 2.3.0
2021-03-28 14:27:59,517:INFO:Initializing setup()
2021-03-28 14:27:59,517:INFO:setup(display=None, profile_kwargs=None, profile=False, verbose=False, silent=True, log_data=False, log_profile=False, log_plots=False, experiment_name=None, log_experiment=False, session_id=None, html=True, custom_pipeline=None, use_gpu=False, n_jobs=-1, fold_groups=None, fold_shuffle=False, fold=10, fold_strategy=kfold, data_split_stratify=False, data_split_shuffle=True, transform_target_method=box-cox, transform_target=False, fix_imbalance_method=None, fix_imbalance=False, interaction_threshold=0.01, feature_ratio=False, feature_interaction=False, feature_selection_method=classic, feature_selection_threshold=0.8, feature_selection=False, group_names=None, group_features=None, polynomial_threshold=0.1, trigonometry_features=False, polynomial_degree=2, polynomial_features=False, cluster_iter=20, create_clusters=False, remove_perfect_collinearity=True, multicollinearity_threshold=0.9, remove_multicollinearity=False, outliers_threshold=0.05, remove_outliers=False, bin_numeric_features=None, rare_level_threshold=0.1, combine_rare_levels=False, ignore_low_variance=False, pca_components=None, pca_method=linear, pca=False, unknown_categorical_method=least_frequent, handle_unknown_categorical=True, transformation_method=yeo-johnson, transformation=False, normalize_method=zscore, normalize=True, ignore_features=['EmployeeNumber', 'StandardHours', 'EmployeeCount', 'Over18'], date_features=None, numeric_iterative_imputer=lightgbm, numeric_imputation=mean, numeric_features=['DistanceFromHome', 'HourlyRate', 'DailyRate', 'MonthlyIncome', 'MonthlyRate', 'NumCompaniesWorked', 'PercentSalaryHike', 'TotalWorkingYears', 'YearsAtCompany', 'YearsInCurrentRole', 'YearsWithCurrManager', 'TrainingTimesLastYear', 'YearsSinceLastPromotion'], high_cardinality_method=frequency, high_cardinality_features=None, ordinal_features={'StockOptionLevel': ['0', '1', '2', '3'], 'EnvironmentSatisfaction': ['1', '2', '3', '4'], 'JobInvolvement': ['1', '2', '3', '4'], 'JobSatisfaction': ['1', '2', '3', '4'], 'Education': ['1', '2', '3', '4', '5'], 'PerformanceRating': ['3', '4'], 'RelationshipSatisfaction': ['1', '2', '3', '4'], 'WorkLifeBalance': ['1', '2', '3', '4']}, categorical_iterative_imputer=lightgbm, categorical_imputation=constant, categorical_features=['BusinessTravel', 'Department', 'EducationField', 'JobRole', 'Gender', 'JobLevel', 'JobRole', 'MaritalStatus', 'OverTime', 'WorkLifeBalance'], iterative_imputation_iters=5, imputation_type=simple, preprocess=True, test_data= Age BusinessTravel DailyRate Department \
1041 28 Travel_Rarely 866 Sales
184 53 Travel_Rarely 1084 Research & Development
1222 24 Travel_Rarely 240 Human Resources
67 45 Travel_Rarely 1339 Research & Development
220 36 Travel_Rarely 1396 Research & Development
... ... ... ... ...
1009 58 Travel_Rarely 1055 Research & Development
757 34 Travel_Rarely 216 Sales
1361 26 Travel_Frequently 1096 Research & Development
376 51 Travel_Rarely 1178 Sales
314 39 Travel_Rarely 117 Research & Development
DistanceFromHome Education EducationField EmployeeCount \
1041 5 3 Medical 1
184 13 2 Medical 1
1222 22 1 Human Resources 1
67 7 3 Life Sciences 1
220 5 2 Life Sciences 1
... ... ... ... ...
1009 1 3 Medical 1
757 1 4 Marketing 1
1361 6 3 Other 1
376 14 2 Life Sciences 1
314 10 1 Medical 1
EmployeeNumber EnvironmentSatisfaction ... StandardHours \
1041 1469 4 ... 80
184 250 4 ... 80
1222 1714 4 ... 80
67 86 2 ... 80
220 304 4 ... 80
... ... ... ... ...
1009 1423 4 ... 80
757 1047 2 ... 80
1361 1918 3 ... 80
376 500 3 ... 80
314 429 3 ... 80
StockOptionLevel TotalWorkingYears TrainingTimesLastYear \
1041 0 6 4
184 2 5 3
1222 1 1 2
67 1 25 2
220 0 16 3
... ... ... ...
1009 1 32 3
757 1 16 2
1361 1 8 3
376 1 18 2
314 0 21 3
WorkLifeBalance YearsAtCompany YearsInCurrentRole \
1041 3 5 4
184 3 4 2
1222 3 1 0
67 3 1 0
220 4 13 11
... ... ... ...
1009 3 9 8
757 2 15 1
1361 3 7 7
376 2 7 7
314 3 21 9
YearsSinceLastPromotion YearsWithCurrManager Attrition
1041 1 3 No
184 1 3 No
1222 0 0 Yes
67 0 0 No
220 3 7 No
... ... ... ...
1009 1 5 No
757 0 9 No
1361 7 7 No
376 0 7 No
314 11 10 No
[588 rows x 35 columns], train_size=0.7, available_plots={'parameter': 'Hyperparameters', 'auc': 'AUC', 'confusion_matrix': 'Confusion Matrix', 'threshold': 'Threshold', 'pr': 'Precision Recall', 'error': 'Prediction Error', 'class_report': 'Class Report', 'rfe': 'Feature Selection', 'learning': 'Learning Curve', 'manifold': 'Manifold Learning', 'calibration': 'Calibration Curve', 'vc': 'Validation Curve', 'dimension': 'Dimensions', 'feature': 'Feature Importance', 'feature_all': 'Feature Importance (All)', 'boundary': 'Decision Boundary', 'lift': 'Lift Chart', 'gain': 'Gain Chart', 'tree': 'Decision Tree'}, ml_usecase=classification, target=Attrition)
2021-03-28 14:27:59,517:INFO:Checking environment
2021-03-28 14:27:59,517:INFO:python_version: 3.6.13
2021-03-28 14:27:59,517:INFO:python_build: ('default', 'Feb 23 2021 21:15:04')
2021-03-28 14:27:59,517:INFO:machine: x86_64
2021-03-28 14:27:59,517:INFO:platform: Linux-4.14.214-160.339.amzn2.x86_64-x86_64-with-glibc2.9
2021-03-28 14:27:59,518:WARNING:cannot find psutil installation. memory not traceable. Install psutil using pip to enable memory logging.
2021-03-28 14:27:59,518:INFO:Checking libraries
2021-03-28 14:27:59,518:INFO:pd==1.1.4
2021-03-28 14:27:59,518:INFO:numpy==1.19.5
2021-03-28 14:27:59,518:INFO:sklearn==0.23.2
2021-03-28 14:27:59,518:INFO:lightgbm==3.2.0
2021-03-28 14:27:59,518:WARNING:catboost not found
2021-03-28 14:27:59,518:WARNING:xgboost not found
2021-03-28 14:27:59,518:INFO:mlflow==1.15.0
2021-03-28 14:27:59,518:INFO:Checking Exceptions
2021-03-28 14:27:59,521:INFO:Declaring global variables
2021-03-28 14:27:59,521:INFO:USI: a7d5
2021-03-28 14:27:59,521:INFO:pycaret_globals: {'_gpu_n_jobs_param', 'transform_target_param', 'seed', 'logging_param', '_all_models', 'y_train', 'gpu_param', 'pycaret_globals', 'experiment__', 'X_test', 'y', '_all_models_internal', 'data_before_preprocess', 'X_train', 'master_model_container', '_all_metrics', '_available_plots', 'log_plots_param', '_ml_usecase', 'exp_name_log', '_internal_pipeline', 'imputation_classifier', 'transform_target_method_param', 'html_param', 'fold_param', 'display_container', 'fix_imbalance_method_param', 'X', 'fold_shuffle_param', 'fix_imbalance_param', 'y_test', 'imputation_regressor', 'stratify_param', 'fold_generator', 'target_param', 'prep_pipe', 'USI', 'create_model_container', 'fold_groups_param', 'iterative_imputation_iters_param', 'n_jobs_param'}
2021-03-28 14:27:59,521:INFO:Preparing display monitor
2021-03-28 14:27:59,521:INFO:Importing libraries
2021-03-28 14:27:59,521:INFO:Copying data for preprocessing
2021-03-28 14:27:59,522:INFO:Declaring preprocessing parameters
2021-03-28 14:27:59,523:INFO:Creating preprocessing pipeline
2021-03-28 14:27:59,539:INFO:Preprocessing pipeline created successfully
2021-03-28 14:27:59,540:ERROR:(Process Exit): setup has been interupted with user command 'quit'. setup must rerun.
2021-03-28 14:27:59,540:INFO:Creating global containers
2021-03-28 14:27:59,540:INFO:Internal pipeline: Pipeline(memory=None, steps=[('empty_step', 'passthrough')], verbose=False)
2021-03-28 14:28:00,178:WARNING:Couldn't import xgboost.XGBClassifier
2021-03-28 14:28:00,179:WARNING:Couldn't import catboost.CatBoostClassifier
2021-03-28 14:28:00,308:WARNING:Couldn't import xgboost.XGBClassifier
2021-03-28 14:28:00,308:WARNING:Couldn't import catboost.CatBoostClassifier
2021-03-28 14:28:00,309:INFO:Creating grid variables
2021-03-28 14:28:00,312:INFO:create_model_container: 0
2021-03-28 14:28:00,312:INFO:master_model_container: 0
2021-03-28 14:28:00,312:INFO:display_container: 1
2021-03-28 14:28:00,322:INFO:Pipeline(memory=None,
steps=[('dtypes',
DataTypes_Auto_infer(categorical_features=['BusinessTravel',
'Department',
'EducationField',
'JobRole', 'Gender',
'JobLevel',
'JobRole',
'MaritalStatus',
'OverTime',
'WorkLifeBalance'],
display_types=False,
features_todrop=['EmployeeNumber',
'StandardHours',
'EmployeeCount',
'Over18'],
id_columns=[],
ml_usecase='classification...
('P_transform', 'passthrough'), ('binn', 'passthrough'),
('rem_outliers', 'passthrough'), ('cluster_all', 'passthrough'),
('dummy', Dummify(target='Attrition')),
('fix_perfect', Remove_100(target='Attrition')),
('clean_names', Clean_Colum_Names()),
('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),
('dfs', 'passthrough'), ('pca', 'passthrough')],
verbose=False)
2021-03-28 14:28:00,322:INFO:setup() succesfully completed......................................
2021-03-28 14:59:39,517:INFO:PyCaret Supervised Module
2021-03-28 14:59:39,517:INFO:ML Usecase: classification
2021-03-28 14:59:39,517:INFO:version 2.3.0
2021-03-28 14:59:39,517:INFO:Initializing setup()
2021-03-28 14:59:39,517:INFO:setup(display=None, profile_kwargs=None, profile=False, verbose=False, silent=True, log_data=False, log_profile=False, log_plots=False, experiment_name=None, log_experiment=False, session_id=None, html=True, custom_pipeline=None, use_gpu=False, n_jobs=-1, fold_groups=None, fold_shuffle=False, fold=10, fold_strategy=kfold, data_split_stratify=False, data_split_shuffle=True, transform_target_method=box-cox, transform_target=False, fix_imbalance_method=None, fix_imbalance=False, interaction_threshold=0.01, feature_ratio=False, feature_interaction=False, feature_selection_method=classic, feature_selection_threshold=0.8, feature_selection=False, group_names=None, group_features=None, polynomial_threshold=0.1, trigonometry_features=False, polynomial_degree=2, polynomial_features=False, cluster_iter=20, create_clusters=False, remove_perfect_collinearity=True, multicollinearity_threshold=0.9, remove_multicollinearity=False, outliers_threshold=0.05, remove_outliers=False, bin_numeric_features=None, rare_level_threshold=0.1, combine_rare_levels=False, ignore_low_variance=False, pca_components=None, pca_method=linear, pca=False, unknown_categorical_method=least_frequent, handle_unknown_categorical=True, transformation_method=yeo-johnson, transformation=False, normalize_method=zscore, normalize=True, ignore_features=['EmployeeNumber', 'StandardHours', 'EmployeeCount', 'Over18'], date_features=None, numeric_iterative_imputer=lightgbm, numeric_imputation=mean, numeric_features=['DistanceFromHome', 'HourlyRate', 'DailyRate', 'MonthlyIncome', 'MonthlyRate', 'NumCompaniesWorked', 'PercentSalaryHike', 'TotalWorkingYears', 'YearsAtCompany', 'YearsInCurrentRole', 'YearsWithCurrManager', 'TrainingTimesLastYear', 'YearsSinceLastPromotion'], high_cardinality_method=frequency, high_cardinality_features=None, ordinal_features={'StockOptionLevel': ['0', '1', '2', '3'], 'EnvironmentSatisfaction': ['1', '2', '3', '4'], 'JobInvolvement': ['1', '2', '3', '4'], 'JobSatisfaction': ['1', '2', '3', '4'], 'Education': ['1', '2', '3', '4', '5'], 'PerformanceRating': ['3', '4'], 'RelationshipSatisfaction': ['1', '2', '3', '4'], 'WorkLifeBalance': ['1', '2', '3', '4']}, categorical_iterative_imputer=lightgbm, categorical_imputation=constant, categorical_features=['BusinessTravel', 'Department', 'EducationField', 'JobRole', 'Gender', 'JobLevel', 'JobRole', 'MaritalStatus', 'OverTime', 'WorkLifeBalance'], iterative_imputation_iters=5, imputation_type=simple, preprocess=True, test_data= Age BusinessTravel DailyRate Department \
1041 28 Travel_Rarely 866 Sales
184 53 Travel_Rarely 1084 Research & Development
1222 24 Travel_Rarely 240 Human Resources
67 45 Travel_Rarely 1339 Research & Development
220 36 Travel_Rarely 1396 Research & Development
... ... ... ... ...
1009 58 Travel_Rarely 1055 Research & Development
757 34 Travel_Rarely 216 Sales
1361 26 Travel_Frequently 1096 Research & Development
376 51 Travel_Rarely 1178 Sales
314 39 Travel_Rarely 117 Research & Development
DistanceFromHome Education EducationField EmployeeCount \
1041 5 3 Medical 1
184 13 2 Medical 1
1222 22 1 Human Resources 1
67 7 3 Life Sciences 1
220 5 2 Life Sciences 1
... ... ... ... ...
1009 1 3 Medical 1
757 1 4 Marketing 1
1361 6 3 Other 1
376 14 2 Life Sciences 1
314 10 1 Medical 1
EmployeeNumber EnvironmentSatisfaction ... StandardHours \
1041 1469 4 ... 80
184 250 4 ... 80
1222 1714 4 ... 80
67 86 2 ... 80
220 304 4 ... 80
... ... ... ... ...
1009 1423 4 ... 80
757 1047 2 ... 80
1361 1918 3 ... 80
376 500 3 ... 80
314 429 3 ... 80
StockOptionLevel TotalWorkingYears TrainingTimesLastYear \
1041 0 6 4
184 2 5 3
1222 1 1 2
67 1 25 2
220 0 16 3
... ... ... ...
1009 1 32 3
757 1 16 2
1361 1 8 3
376 1 18 2
314 0 21 3
WorkLifeBalance YearsAtCompany YearsInCurrentRole \
1041 3 5 4
184 3 4 2
1222 3 1 0
67 3 1 0
220 4 13 11
... ... ... ...
1009 3 9 8
757 2 15 1
1361 3 7 7
376 2 7 7
314 3 21 9
YearsSinceLastPromotion YearsWithCurrManager Attrition
1041 1 3 No
184 1 3 No
1222 0 0 Yes
67 0 0 No
220 3 7 No
... ... ... ...
1009 1 5 No
757 0 9 No
1361 7 7 No
376 0 7 No
314 11 10 No
[588 rows x 35 columns], train_size=0.7, available_plots={'parameter': 'Hyperparameters', 'auc': 'AUC', 'confusion_matrix': 'Confusion Matrix', 'threshold': 'Threshold', 'pr': 'Precision Recall', 'error': 'Prediction Error', 'class_report': 'Class Report', 'rfe': 'Feature Selection', 'learning': 'Learning Curve', 'manifold': 'Manifold Learning', 'calibration': 'Calibration Curve', 'vc': 'Validation Curve', 'dimension': 'Dimensions', 'feature': 'Feature Importance', 'feature_all': 'Feature Importance (All)', 'boundary': 'Decision Boundary', 'lift': 'Lift Chart', 'gain': 'Gain Chart', 'tree': 'Decision Tree'}, ml_usecase=classification, target=Attrition)
2021-03-28 14:59:39,517:INFO:Checking environment
2021-03-28 14:59:39,517:INFO:python_version: 3.6.13
2021-03-28 14:59:39,517:INFO:python_build: ('default', 'Feb 23 2021 21:15:04')
2021-03-28 14:59:39,517:INFO:machine: x86_64
2021-03-28 14:59:39,518:INFO:platform: Linux-4.14.214-160.339.amzn2.x86_64-x86_64-with-glibc2.9
2021-03-28 14:59:39,518:WARNING:cannot find psutil installation. memory not traceable. Install psutil using pip to enable memory logging.
2021-03-28 14:59:39,518:INFO:Checking libraries
2021-03-28 14:59:39,518:INFO:pd==1.1.4
2021-03-28 14:59:39,518:INFO:numpy==1.19.5
2021-03-28 14:59:39,518:INFO:sklearn==0.23.2
2021-03-28 14:59:39,518:INFO:lightgbm==3.2.0
2021-03-28 14:59:39,518:WARNING:catboost not found
2021-03-28 14:59:39,519:WARNING:xgboost not found
2021-03-28 14:59:39,519:INFO:mlflow==1.15.0
2021-03-28 14:59:39,519:INFO:Checking Exceptions
2021-03-28 14:59:39,521:INFO:Declaring global variables
2021-03-28 14:59:39,521:INFO:USI: a168
2021-03-28 14:59:39,521:INFO:pycaret_globals: {'_gpu_n_jobs_param', 'transform_target_param', 'seed', 'logging_param', '_all_models', 'y_train', 'gpu_param', 'pycaret_globals', 'experiment__', 'X_test', 'y', '_all_models_internal', 'data_before_preprocess', 'X_train', 'master_model_container', '_all_metrics', '_available_plots', 'log_plots_param', '_ml_usecase', 'exp_name_log', '_internal_pipeline', 'imputation_classifier', 'transform_target_method_param', 'html_param', 'fold_param', 'display_container', 'fix_imbalance_method_param', 'X', 'fold_shuffle_param', 'fix_imbalance_param', 'y_test', 'imputation_regressor', 'stratify_param', 'fold_generator', 'target_param', 'prep_pipe', 'USI', 'create_model_container', 'fold_groups_param', 'iterative_imputation_iters_param', 'n_jobs_param'}
2021-03-28 14:59:39,521:INFO:Preparing display monitor
2021-03-28 14:59:39,522:INFO:Importing libraries
2021-03-28 14:59:39,522:INFO:Copying data for preprocessing
2021-03-28 14:59:39,522:INFO:Declaring preprocessing parameters
2021-03-28 14:59:39,523:INFO:Creating preprocessing pipeline
2021-03-28 14:59:39,540:INFO:Preprocessing pipeline created successfully
2021-03-28 14:59:39,540:ERROR:(Process Exit): setup has been interupted with user command 'quit'. setup must rerun.
2021-03-28 14:59:39,540:INFO:Creating global containers
2021-03-28 14:59:39,540:INFO:Internal pipeline: Pipeline(memory=None, steps=[('empty_step', 'passthrough')], verbose=False)
2021-03-28 14:59:40,182:WARNING:Couldn't import xgboost.XGBClassifier
2021-03-28 14:59:40,183:WARNING:Couldn't import catboost.CatBoostClassifier
2021-03-28 14:59:40,314:WARNING:Couldn't import xgboost.XGBClassifier
2021-03-28 14:59:40,315:WARNING:Couldn't import catboost.CatBoostClassifier
2021-03-28 14:59:40,315:INFO:Creating grid variables
2021-03-28 14:59:40,319:INFO:create_model_container: 0
2021-03-28 14:59:40,319:INFO:master_model_container: 0
2021-03-28 14:59:40,319:INFO:display_container: 1
2021-03-28 14:59:40,329:INFO:Pipeline(memory=None,
steps=[('dtypes',
DataTypes_Auto_infer(categorical_features=['BusinessTravel',
'Department',
'EducationField',
'JobRole', 'Gender',
'JobLevel',
'JobRole',
'MaritalStatus',
'OverTime',
'WorkLifeBalance'],
display_types=False,
features_todrop=['EmployeeNumber',
'StandardHours',
'EmployeeCount',
'Over18'],
id_columns=[],
ml_usecase='classification...
('P_transform', 'passthrough'), ('binn', 'passthrough'),
('rem_outliers', 'passthrough'), ('cluster_all', 'passthrough'),
('dummy', Dummify(target='Attrition')),
('fix_perfect', Remove_100(target='Attrition')),
('clean_names', Clean_Colum_Names()),
('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),
('dfs', 'passthrough'), ('pca', 'passthrough')],
verbose=False)
2021-03-28 14:59:40,329:INFO:setup() succesfully completed......................................
2021-03-28 15:06:46,070:INFO:PyCaret Supervised Module
2021-03-28 15:06:46,070:INFO:ML Usecase: classification
2021-03-28 15:06:46,070:INFO:version 2.3.0
2021-03-28 15:06:46,070:INFO:Initializing setup()
2021-03-28 15:06:46,070:INFO:setup(display=None, profile_kwargs=None, profile=False, verbose=False, silent=True, log_data=False, log_profile=False, log_plots=False, experiment_name=None, log_experiment=False, session_id=None, html=True, custom_pipeline=None, use_gpu=False, n_jobs=-1, fold_groups=None, fold_shuffle=False, fold=10, fold_strategy=kfold, data_split_stratify=False, data_split_shuffle=True, transform_target_method=box-cox, transform_target=False, fix_imbalance_method=None, fix_imbalance=False, interaction_threshold=0.01, feature_ratio=False, feature_interaction=False, feature_selection_method=classic, feature_selection_threshold=0.8, feature_selection=False, group_names=None, group_features=None, polynomial_threshold=0.1, trigonometry_features=False, polynomial_degree=2, polynomial_features=False, cluster_iter=20, create_clusters=False, remove_perfect_collinearity=True, multicollinearity_threshold=0.9, remove_multicollinearity=False, outliers_threshold=0.05, remove_outliers=False, bin_numeric_features=None, rare_level_threshold=0.1, combine_rare_levels=False, ignore_low_variance=False, pca_components=None, pca_method=linear, pca=False, unknown_categorical_method=least_frequent, handle_unknown_categorical=True, transformation_method=yeo-johnson, transformation=False, normalize_method=zscore, normalize=True, ignore_features=['EmployeeNumber', 'StandardHours', 'EmployeeCount', 'Over18'], date_features=None, numeric_iterative_imputer=lightgbm, numeric_imputation=mean, numeric_features=['DistanceFromHome', 'HourlyRate', 'DailyRate', 'MonthlyIncome', 'MonthlyRate', 'NumCompaniesWorked', 'PercentSalaryHike', 'TotalWorkingYears', 'YearsAtCompany', 'YearsInCurrentRole', 'YearsWithCurrManager', 'TrainingTimesLastYear', 'YearsSinceLastPromotion'], high_cardinality_method=frequency, high_cardinality_features=None, ordinal_features={'StockOptionLevel': ['0', '1', '2', '3'], 'EnvironmentSatisfaction': ['1', '2', '3', '4'], 'JobInvolvement': ['1', '2', '3', '4'], 'JobSatisfaction': ['1', '2', '3', '4'], 'Education': ['1', '2', '3', '4', '5'], 'PerformanceRating': ['3', '4'], 'RelationshipSatisfaction': ['1', '2', '3', '4'], 'WorkLifeBalance': ['1', '2', '3', '4']}, categorical_iterative_imputer=lightgbm, categorical_imputation=constant, categorical_features=['BusinessTravel', 'Department', 'EducationField', 'JobRole', 'Gender', 'JobLevel', 'JobRole', 'MaritalStatus', 'OverTime', 'WorkLifeBalance'], iterative_imputation_iters=5, imputation_type=simple, preprocess=True, test_data= Age BusinessTravel DailyRate Department \
1041 28 Travel_Rarely 866 Sales
184 53 Travel_Rarely 1084 Research & Development
1222 24 Travel_Rarely 240 Human Resources
67 45 Travel_Rarely 1339 Research & Development
220 36 Travel_Rarely 1396 Research & Development
... ... ... ... ...
1009 58 Travel_Rarely 1055 Research & Development
757 34 Travel_Rarely 216 Sales
1361 26 Travel_Frequently 1096 Research & Development
376 51 Travel_Rarely 1178 Sales
314 39 Travel_Rarely 117 Research & Development
DistanceFromHome Education EducationField EmployeeCount \
1041 5 3 Medical 1
184 13 2 Medical 1
1222 22 1 Human Resources 1
67 7 3 Life Sciences 1
220 5 2 Life Sciences 1
... ... ... ... ...
1009 1 3 Medical 1
757 1 4 Marketing 1
1361 6 3 Other 1
376 14 2 Life Sciences 1
314 10 1 Medical 1
EmployeeNumber EnvironmentSatisfaction ... StandardHours \
1041 1469 4 ... 80
184 250 4 ... 80
1222 1714 4 ... 80
67 86 2 ... 80
220 304 4 ... 80
... ... ... ... ...
1009 1423 4 ... 80
757 1047 2 ... 80
1361 1918 3 ... 80
376 500 3 ... 80
314 429 3 ... 80
StockOptionLevel TotalWorkingYears TrainingTimesLastYear \
1041 0 6 4
184 2 5 3
1222 1 1 2
67 1 25 2
220 0 16 3
... ... ... ...
1009 1 32 3
757 1 16 2
1361 1 8 3
376 1 18 2
314 0 21 3
WorkLifeBalance YearsAtCompany YearsInCurrentRole \
1041 3 5 4
184 3 4 2
1222 3 1 0
67 3 1 0
220 4 13 11
... ... ... ...
1009 3 9 8
757 2 15 1
1361 3 7 7
376 2 7 7
314 3 21 9
YearsSinceLastPromotion YearsWithCurrManager Attrition
1041 1 3 No
184 1 3 No
1222 0 0 Yes
67 0 0 No
220 3 7 No
... ... ... ...
1009 1 5 No
757 0 9 No
1361 7 7 No
376 0 7 No
314 11 10 No
[588 rows x 35 columns], train_size=0.7, available_plots={'parameter': 'Hyperparameters', 'auc': 'AUC', 'confusion_matrix': 'Confusion Matrix', 'threshold': 'Threshold', 'pr': 'Precision Recall', 'error': 'Prediction Error', 'class_report': 'Class Report', 'rfe': 'Feature Selection', 'learning': 'Learning Curve', 'manifold': 'Manifold Learning', 'calibration': 'Calibration Curve', 'vc': 'Validation Curve', 'dimension': 'Dimensions', 'feature': 'Feature Importance', 'feature_all': 'Feature Importance (All)', 'boundary': 'Decision Boundary', 'lift': 'Lift Chart', 'gain': 'Gain Chart', 'tree': 'Decision Tree'}, ml_usecase=classification, target=Attrition)
2021-03-28 15:06:46,070:INFO:Checking environment
2021-03-28 15:06:46,070:INFO:python_version: 3.6.13
2021-03-28 15:06:46,070:INFO:python_build: ('default', 'Feb 23 2021 21:15:04')
2021-03-28 15:06:46,070:INFO:machine: x86_64
2021-03-28 15:06:46,070:INFO:platform: Linux-4.14.214-160.339.amzn2.x86_64-x86_64-with-glibc2.9
2021-03-28 15:06:46,071:WARNING:cannot find psutil installation. memory not traceable. Install psutil using pip to enable memory logging.
2021-03-28 15:06:46,071:INFO:Checking libraries
2021-03-28 15:06:46,071:INFO:pd==1.1.4
2021-03-28 15:06:46,071:INFO:numpy==1.19.5
2021-03-28 15:06:46,071:INFO:sklearn==0.23.2
2021-03-28 15:06:46,071:INFO:lightgbm==3.2.0
2021-03-28 15:06:46,071:WARNING:catboost not found
2021-03-28 15:06:46,071:WARNING:xgboost not found
2021-03-28 15:06:46,072:INFO:mlflow==1.15.0
2021-03-28 15:06:46,072:INFO:Checking Exceptions
2021-03-28 15:06:46,074:INFO:Declaring global variables
2021-03-28 15:06:46,074:INFO:USI: e6f7
2021-03-28 15:06:46,074:INFO:pycaret_globals: {'_gpu_n_jobs_param', 'transform_target_param', 'seed', 'logging_param', '_all_models', 'y_train', 'gpu_param', 'pycaret_globals', 'experiment__', 'X_test', 'y', '_all_models_internal', 'data_before_preprocess', 'X_train', 'master_model_container', '_all_metrics', '_available_plots', 'log_plots_param', '_ml_usecase', 'exp_name_log', '_internal_pipeline', 'imputation_classifier', 'transform_target_method_param', 'html_param', 'fold_param', 'display_container', 'fix_imbalance_method_param', 'X', 'fold_shuffle_param', 'fix_imbalance_param', 'y_test', 'imputation_regressor', 'stratify_param', 'fold_generator', 'target_param', 'prep_pipe', 'USI', 'create_model_container', 'fold_groups_param', 'iterative_imputation_iters_param', 'n_jobs_param'}
2021-03-28 15:06:46,074:INFO:Preparing display monitor
2021-03-28 15:06:46,074:INFO:Importing libraries
2021-03-28 15:06:46,074:INFO:Copying data for preprocessing
2021-03-28 15:06:46,075:INFO:Declaring preprocessing parameters
2021-03-28 15:06:46,076:INFO:Creating preprocessing pipeline
2021-03-28 15:06:46,092:INFO:Preprocessing pipeline created successfully
2021-03-28 15:06:46,092:ERROR:(Process Exit): setup has been interupted with user command 'quit'. setup must rerun.
2021-03-28 15:06:46,092:INFO:Creating global containers
2021-03-28 15:06:46,093:INFO:Internal pipeline: Pipeline(memory=None, steps=[('empty_step', 'passthrough')], verbose=False)
2021-03-28 15:06:46,726:WARNING:Couldn't import xgboost.XGBClassifier
2021-03-28 15:06:46,726:WARNING:Couldn't import catboost.CatBoostClassifier
2021-03-28 15:06:46,857:WARNING:Couldn't import xgboost.XGBClassifier
2021-03-28 15:06:46,857:WARNING:Couldn't import catboost.CatBoostClassifier
2021-03-28 15:06:46,858:INFO:Creating grid variables
2021-03-28 15:06:46,861:INFO:create_model_container: 0
2021-03-28 15:06:46,861:INFO:master_model_container: 0
2021-03-28 15:06:46,861:INFO:display_container: 1
2021-03-28 15:06:46,871:INFO:Pipeline(memory=None,
steps=[('dtypes',
DataTypes_Auto_infer(categorical_features=['BusinessTravel',
'Department',
'EducationField',
'JobRole', 'Gender',
'JobLevel',
'JobRole',
'MaritalStatus',
'OverTime',
'WorkLifeBalance'],
display_types=False,
features_todrop=['EmployeeNumber',
'StandardHours',
'EmployeeCount',
'Over18'],
id_columns=[],
ml_usecase='classification...
('P_transform', 'passthrough'), ('binn', 'passthrough'),
('rem_outliers', 'passthrough'), ('cluster_all', 'passthrough'),
('dummy', Dummify(target='Attrition')),
('fix_perfect', Remove_100(target='Attrition')),
('clean_names', Clean_Colum_Names()),
('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),
('dfs', 'passthrough'), ('pca', 'passthrough')],
verbose=False)
2021-03-28 15:06:46,871:INFO:setup() succesfully completed......................................
2021-03-28 15:07:10,220:INFO:PyCaret Supervised Module
2021-03-28 15:07:10,220:INFO:ML Usecase: classification
2021-03-28 15:07:10,220:INFO:version 2.3.0
2021-03-28 15:07:10,220:INFO:Initializing setup()
2021-03-28 15:07:10,220:INFO:setup(display=None, profile_kwargs=None, profile=False, verbose=False, silent=True, log_data=False, log_profile=False, log_plots=False, experiment_name=None, log_experiment=False, session_id=None, html=True, custom_pipeline=None, use_gpu=False, n_jobs=-1, fold_groups=None, fold_shuffle=False, fold=10, fold_strategy=kfold, data_split_stratify=False, data_split_shuffle=True, transform_target_method=box-cox, transform_target=False, fix_imbalance_method=None, fix_imbalance=False, interaction_threshold=0.01, feature_ratio=False, feature_interaction=False, feature_selection_method=classic, feature_selection_threshold=0.8, feature_selection=False, group_names=None, group_features=None, polynomial_threshold=0.1, trigonometry_features=False, polynomial_degree=2, polynomial_features=False, cluster_iter=20, create_clusters=False, remove_perfect_collinearity=True, multicollinearity_threshold=0.9, remove_multicollinearity=False, outliers_threshold=0.05, remove_outliers=False, bin_numeric_features=None, rare_level_threshold=0.1, combine_rare_levels=False, ignore_low_variance=False, pca_components=None, pca_method=linear, pca=False, unknown_categorical_method=least_frequent, handle_unknown_categorical=True, transformation_method=yeo-johnson, transformation=False, normalize_method=zscore, normalize=True, ignore_features=['EmployeeNumber', 'StandardHours', 'EmployeeCount', 'Over18'], date_features=None, numeric_iterative_imputer=lightgbm, numeric_imputation=mean, numeric_features=['DistanceFromHome', 'HourlyRate', 'DailyRate', 'MonthlyIncome', 'MonthlyRate', 'NumCompaniesWorked', 'PercentSalaryHike', 'TotalWorkingYears', 'YearsAtCompany', 'YearsInCurrentRole', 'YearsWithCurrManager', 'TrainingTimesLastYear', 'YearsSinceLastPromotion'], high_cardinality_method=frequency, high_cardinality_features=None, ordinal_features={'StockOptionLevel': ['0', '1', '2', '3'], 'EnvironmentSatisfaction': ['1', '2', '3', '4'], 'JobInvolvement': ['1', '2', '3', '4'], 'JobSatisfaction': ['1', '2', '3', '4'], 'Education': ['1', '2', '3', '4', '5'], 'PerformanceRating': ['3', '4'], 'RelationshipSatisfaction': ['1', '2', '3', '4'], 'WorkLifeBalance': ['1', '2', '3', '4']}, categorical_iterative_imputer=lightgbm, categorical_imputation=constant, categorical_features=['BusinessTravel', 'Department', 'EducationField', 'JobRole', 'Gender', 'JobLevel', 'JobRole', 'MaritalStatus', 'OverTime', 'WorkLifeBalance'], iterative_imputation_iters=5, imputation_type=simple, preprocess=True, test_data= Age BusinessTravel DailyRate Department \
1041 28 Travel_Rarely 866 Sales
184 53 Travel_Rarely 1084 Research & Development
1222 24 Travel_Rarely 240 Human Resources
67 45 Travel_Rarely 1339 Research & Development
220 36 Travel_Rarely 1396 Research & Development
... ... ... ... ...
1009 58 Travel_Rarely 1055 Research & Development
757 34 Travel_Rarely 216 Sales
1361 26 Travel_Frequently 1096 Research & Development
376 51 Travel_Rarely 1178 Sales
314 39 Travel_Rarely 117 Research & Development
DistanceFromHome Education EducationField EmployeeCount \
1041 5 3 Medical 1
184 13 2 Medical 1
1222 22 1 Human Resources 1
67 7 3 Life Sciences 1
220 5 2 Life Sciences 1
... ... ... ... ...
1009 1 3 Medical 1
757 1 4 Marketing 1
1361 6 3 Other 1
376 14 2 Life Sciences 1
314 10 1 Medical 1
EmployeeNumber EnvironmentSatisfaction ... StandardHours \
1041 1469 4 ... 80
184 250 4 ... 80
1222 1714 4 ... 80
67 86 2 ... 80
220 304 4 ... 80
... ... ... ... ...
1009 1423 4 ... 80
757 1047 2 ... 80
1361 1918 3 ... 80
376 500 3 ... 80
314 429 3 ... 80
StockOptionLevel TotalWorkingYears TrainingTimesLastYear \
1041 0 6 4
184 2 5 3
1222 1 1 2
67 1 25 2
220 0 16 3
... ... ... ...
1009 1 32 3
757 1 16 2
1361 1 8 3
376 1 18 2
314 0 21 3
WorkLifeBalance YearsAtCompany YearsInCurrentRole \
1041 3 5 4
184 3 4 2
1222 3 1 0
67 3 1 0
220 4 13 11
... ... ... ...
1009 3 9 8
757 2 15 1
1361 3 7 7
376 2 7 7
314 3 21 9
YearsSinceLastPromotion YearsWithCurrManager Attrition
1041 1 3 No
184 1 3 No
1222 0 0 Yes
67 0 0 No
220 3 7 No
... ... ... ...
1009 1 5 No
757 0 9 No
1361 7 7 No
376 0 7 No
314 11 10 No
[588 rows x 35 columns], train_size=0.7, available_plots={'parameter': 'Hyperparameters', 'auc': 'AUC', 'confusion_matrix': 'Confusion Matrix', 'threshold': 'Threshold', 'pr': 'Precision Recall', 'error': 'Prediction Error', 'class_report': 'Class Report', 'rfe': 'Feature Selection', 'learning': 'Learning Curve', 'manifold': 'Manifold Learning', 'calibration': 'Calibration Curve', 'vc': 'Validation Curve', 'dimension': 'Dimensions', 'feature': 'Feature Importance', 'feature_all': 'Feature Importance (All)', 'boundary': 'Decision Boundary', 'lift': 'Lift Chart', 'gain': 'Gain Chart', 'tree': 'Decision Tree'}, ml_usecase=classification, target=Attrition)
2021-03-28 15:07:10,220:INFO:Checking environment
2021-03-28 15:07:10,220:INFO:python_version: 3.6.13
2021-03-28 15:07:10,220:INFO:python_build: ('default', 'Feb 23 2021 21:15:04')
2021-03-28 15:07:10,221:INFO:machine: x86_64
2021-03-28 15:07:10,221:INFO:platform: Linux-4.14.214-160.339.amzn2.x86_64-x86_64-with-glibc2.9
2021-03-28 15:07:10,221:WARNING:cannot find psutil installation. memory not traceable. Install psutil using pip to enable memory logging.
2021-03-28 15:07:10,221:INFO:Checking libraries
2021-03-28 15:07:10,221:INFO:pd==1.1.4
2021-03-28 15:07:10,221:INFO:numpy==1.19.5
2021-03-28 15:07:10,221:INFO:sklearn==0.23.2
2021-03-28 15:07:10,221:INFO:lightgbm==3.2.0
2021-03-28 15:07:10,221:WARNING:catboost not found
2021-03-28 15:07:10,221:WARNING:xgboost not found
2021-03-28 15:07:10,222:INFO:mlflow==1.15.0
2021-03-28 15:07:10,222:INFO:Checking Exceptions
2021-03-28 15:07:10,224:INFO:Declaring global variables
2021-03-28 15:07:10,224:INFO:USI: 119f
2021-03-28 15:07:10,224:INFO:pycaret_globals: {'_gpu_n_jobs_param', 'transform_target_param', 'seed', 'logging_param', '_all_models', 'y_train', 'gpu_param', 'pycaret_globals', 'experiment__', 'X_test', 'y', '_all_models_internal', 'data_before_preprocess', 'X_train', 'master_model_container', '_all_metrics', '_available_plots', 'log_plots_param', '_ml_usecase', 'exp_name_log', '_internal_pipeline', 'imputation_classifier', 'transform_target_method_param', 'html_param', 'fold_param', 'display_container', 'fix_imbalance_method_param', 'X', 'fold_shuffle_param', 'fix_imbalance_param', 'y_test', 'imputation_regressor', 'stratify_param', 'fold_generator', 'target_param', 'prep_pipe', 'USI', 'create_model_container', 'fold_groups_param', 'iterative_imputation_iters_param', 'n_jobs_param'}
2021-03-28 15:07:10,224:INFO:Preparing display monitor
2021-03-28 15:07:10,224:INFO:Importing libraries
2021-03-28 15:07:10,224:INFO:Copying data for preprocessing
2021-03-28 15:07:10,225:INFO:Declaring preprocessing parameters
2021-03-28 15:07:10,226:INFO:Creating preprocessing pipeline
2021-03-28 15:07:10,242:INFO:Preprocessing pipeline created successfully
2021-03-28 15:07:10,242:ERROR:(Process Exit): setup has been interupted with user command 'quit'. setup must rerun.
2021-03-28 15:07:10,242:INFO:Creating global containers
2021-03-28 15:07:10,243:INFO:Internal pipeline: Pipeline(memory=None, steps=[('empty_step', 'passthrough')], verbose=False)
2021-03-28 15:07:10,881:WARNING:Couldn't import xgboost.XGBClassifier
2021-03-28 15:07:10,881:WARNING:Couldn't import catboost.CatBoostClassifier
2021-03-28 15:07:11,012:WARNING:Couldn't import xgboost.XGBClassifier
2021-03-28 15:07:11,013:WARNING:Couldn't import catboost.CatBoostClassifier
2021-03-28 15:07:11,013:INFO:Creating grid variables
2021-03-28 15:07:11,016:INFO:create_model_container: 0
2021-03-28 15:07:11,016:INFO:master_model_container: 0
2021-03-28 15:07:11,016:INFO:display_container: 1
2021-03-28 15:07:11,026:INFO:Pipeline(memory=None,
steps=[('dtypes',
DataTypes_Auto_infer(categorical_features=['BusinessTravel',
'Department',
'EducationField',
'JobRole', 'Gender',
'JobLevel',
'JobRole',
'MaritalStatus',
'OverTime',
'WorkLifeBalance'],
display_types=False,
features_todrop=['EmployeeNumber',
'StandardHours',
'EmployeeCount',
'Over18'],
id_columns=[],
ml_usecase='classification...
('P_transform', 'passthrough'), ('binn', 'passthrough'),
('rem_outliers', 'passthrough'), ('cluster_all', 'passthrough'),
('dummy', Dummify(target='Attrition')),
('fix_perfect', Remove_100(target='Attrition')),
('clean_names', Clean_Colum_Names()),
('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),
('dfs', 'passthrough'), ('pca', 'passthrough')],
verbose=False)
2021-03-28 15:07:11,026:INFO:setup() succesfully completed......................................
2021-03-28 15:09:59,132:INFO:PyCaret Supervised Module
2021-03-28 15:09:59,132:INFO:ML Usecase: classification
2021-03-28 15:09:59,132:INFO:version 2.3.0
2021-03-28 15:09:59,132:INFO:Initializing setup()
2021-03-28 15:09:59,132:INFO:setup(display=None, profile_kwargs=None, profile=False, verbose=False, silent=True, log_data=False, log_profile=False, log_plots=False, experiment_name=None, log_experiment=False, session_id=None, html=True, custom_pipeline=None, use_gpu=False, n_jobs=-1, fold_groups=None, fold_shuffle=False, fold=10, fold_strategy=kfold, data_split_stratify=False, data_split_shuffle=True, transform_target_method=box-cox, transform_target=False, fix_imbalance_method=None, fix_imbalance=False, interaction_threshold=0.01, feature_ratio=False, feature_interaction=False, feature_selection_method=classic, feature_selection_threshold=0.8, feature_selection=False, group_names=None, group_features=None, polynomial_threshold=0.1, trigonometry_features=False, polynomial_degree=2, polynomial_features=False, cluster_iter=20, create_clusters=False, remove_perfect_collinearity=True, multicollinearity_threshold=0.9, remove_multicollinearity=False, outliers_threshold=0.05, remove_outliers=False, bin_numeric_features=None, rare_level_threshold=0.1, combine_rare_levels=False, ignore_low_variance=False, pca_components=None, pca_method=linear, pca=False, unknown_categorical_method=least_frequent, handle_unknown_categorical=True, transformation_method=yeo-johnson, transformation=False, normalize_method=zscore, normalize=True, ignore_features=['EmployeeNumber', 'StandardHours', 'EmployeeCount', 'Over18'], date_features=None, numeric_iterative_imputer=lightgbm, numeric_imputation=mean, numeric_features=['DistanceFromHome', 'HourlyRate', 'DailyRate', 'MonthlyIncome', 'MonthlyRate', 'NumCompaniesWorked', 'PercentSalaryHike', 'TotalWorkingYears', 'YearsAtCompany', 'YearsInCurrentRole', 'YearsWithCurrManager', 'TrainingTimesLastYear', 'YearsSinceLastPromotion'], high_cardinality_method=frequency, high_cardinality_features=None, ordinal_features={'StockOptionLevel': ['0', '1', '2', '3'], 'EnvironmentSatisfaction': ['1', '2', '3', '4'], 'JobInvolvement': ['1', '2', '3', '4'], 'JobSatisfaction': ['1', '2', '3', '4'], 'Education': ['1', '2', '3', '4', '5'], 'PerformanceRating': ['3', '4'], 'RelationshipSatisfaction': ['1', '2', '3', '4'], 'WorkLifeBalance': ['1', '2', '3', '4']}, categorical_iterative_imputer=lightgbm, categorical_imputation=constant, categorical_features=['BusinessTravel', 'Department', 'EducationField', 'JobRole', 'Gender', 'JobLevel', 'JobRole', 'MaritalStatus', 'OverTime', 'WorkLifeBalance'], iterative_imputation_iters=5, imputation_type=simple, preprocess=True, test_data= Age BusinessTravel DailyRate Department \
1041 28 Travel_Rarely 866 Sales
184 53 Travel_Rarely 1084 Research & Development
1222 24 Travel_Rarely 240 Human Resources
67 45 Travel_Rarely 1339 Research & Development
220 36 Travel_Rarely 1396 Research & Development
... ... ... ... ...
1009 58 Travel_Rarely 1055 Research & Development
757 34 Travel_Rarely 216 Sales
1361 26 Travel_Frequently 1096 Research & Development
376 51 Travel_Rarely 1178 Sales
314 39 Travel_Rarely 117 Research & Development
DistanceFromHome Education EducationField EmployeeCount \
1041 5 3 Medical 1
184 13 2 Medical 1
1222 22 1 Human Resources 1
67 7 3 Life Sciences 1
220 5 2 Life Sciences 1
... ... ... ... ...
1009 1 3 Medical 1
757 1 4 Marketing 1
1361 6 3 Other 1
376 14 2 Life Sciences 1
314 10 1 Medical 1
EmployeeNumber EnvironmentSatisfaction ... StandardHours \
1041 1469 4 ... 80
184 250 4 ... 80
1222 1714 4 ... 80
67 86 2 ... 80
220 304 4 ... 80
... ... ... ... ...
1009 1423 4 ... 80
757 1047 2 ... 80
1361 1918 3 ... 80
376 500 3 ... 80
314 429 3 ... 80
StockOptionLevel TotalWorkingYears TrainingTimesLastYear \
1041 0 6 4
184 2 5 3
1222 1 1 2
67 1 25 2
220 0 16 3
... ... ... ...
1009 1 32 3
757 1 16 2
1361 1 8 3
376 1 18 2
314 0 21 3
WorkLifeBalance YearsAtCompany YearsInCurrentRole \
1041 3 5 4
184 3 4 2
1222 3 1 0
67 3 1 0
220 4 13 11
... ... ... ...
1009 3 9 8
757 2 15 1
1361 3 7 7
376 2 7 7
314 3 21 9
YearsSinceLastPromotion YearsWithCurrManager Attrition
1041 1 3 No
184 1 3 No
1222 0 0 Yes
67 0 0 No
220 3 7 No
... ... ... ...
1009 1 5 No
757 0 9 No
1361 7 7 No
376 0 7 No
314 11 10 No
[588 rows x 35 columns], train_size=0.7, available_plots={'parameter': 'Hyperparameters', 'auc': 'AUC', 'confusion_matrix': 'Confusion Matrix', 'threshold': 'Threshold', 'pr': 'Precision Recall', 'error': 'Prediction Error', 'class_report': 'Class Report', 'rfe': 'Feature Selection', 'learning': 'Learning Curve', 'manifold': 'Manifold Learning', 'calibration': 'Calibration Curve', 'vc': 'Validation Curve', 'dimension': 'Dimensions', 'feature': 'Feature Importance', 'feature_all': 'Feature Importance (All)', 'boundary': 'Decision Boundary', 'lift': 'Lift Chart', 'gain': 'Gain Chart', 'tree': 'Decision Tree'}, ml_usecase=classification, target=Attrition)
2021-03-28 15:09:59,132:INFO:Checking environment
2021-03-28 15:09:59,132:INFO:python_version: 3.6.13
2021-03-28 15:09:59,132:INFO:python_build: ('default', 'Feb 23 2021 21:15:04')
2021-03-28 15:09:59,133:INFO:machine: x86_64
2021-03-28 15:09:59,133:INFO:platform: Linux-4.14.214-160.339.amzn2.x86_64-x86_64-with-glibc2.9
2021-03-28 15:09:59,133:WARNING:cannot find psutil installation. memory not traceable. Install psutil using pip to enable memory logging.
2021-03-28 15:09:59,133:INFO:Checking libraries
2021-03-28 15:09:59,133:INFO:pd==1.1.4
2021-03-28 15:09:59,133:INFO:numpy==1.19.5
2021-03-28 15:09:59,133:INFO:sklearn==0.23.2
2021-03-28 15:09:59,133:INFO:lightgbm==3.2.0
2021-03-28 15:09:59,133:WARNING:catboost not found
2021-03-28 15:09:59,134:WARNING:xgboost not found
2021-03-28 15:09:59,134:INFO:mlflow==1.15.0
2021-03-28 15:09:59,134:INFO:Checking Exceptions
2021-03-28 15:09:59,136:INFO:Declaring global variables
2021-03-28 15:09:59,136:INFO:USI: b538
2021-03-28 15:09:59,136:INFO:pycaret_globals: {'_gpu_n_jobs_param', 'transform_target_param', 'seed', 'logging_param', '_all_models', 'y_train', 'gpu_param', 'pycaret_globals', 'experiment__', 'X_test', 'y', '_all_models_internal', 'data_before_preprocess', 'X_train', 'master_model_container', '_all_metrics', '_available_plots', 'log_plots_param', '_ml_usecase', 'exp_name_log', '_internal_pipeline', 'imputation_classifier', 'transform_target_method_param', 'html_param', 'fold_param', 'display_container', 'fix_imbalance_method_param', 'X', 'fold_shuffle_param', 'fix_imbalance_param', 'y_test', 'imputation_regressor', 'stratify_param', 'fold_generator', 'target_param', 'prep_pipe', 'USI', 'create_model_container', 'fold_groups_param', 'iterative_imputation_iters_param', 'n_jobs_param'}
2021-03-28 15:09:59,136:INFO:Preparing display monitor
2021-03-28 15:09:59,136:INFO:Importing libraries
2021-03-28 15:09:59,137:INFO:Copying data for preprocessing
2021-03-28 15:09:59,137:INFO:Declaring preprocessing parameters
2021-03-28 15:09:59,138:INFO:Creating preprocessing pipeline
2021-03-28 15:09:59,154:INFO:Preprocessing pipeline created successfully
2021-03-28 15:09:59,155:ERROR:(Process Exit): setup has been interupted with user command 'quit'. setup must rerun.
2021-03-28 15:09:59,155:INFO:Creating global containers
2021-03-28 15:09:59,155:INFO:Internal pipeline: Pipeline(memory=None, steps=[('empty_step', 'passthrough')], verbose=False)
2021-03-28 15:09:59,786:WARNING:Couldn't import xgboost.XGBClassifier
2021-03-28 15:09:59,787:WARNING:Couldn't import catboost.CatBoostClassifier
2021-03-28 15:09:59,918:WARNING:Couldn't import xgboost.XGBClassifier
2021-03-28 15:09:59,918:WARNING:Couldn't import catboost.CatBoostClassifier
2021-03-28 15:09:59,919:INFO:Creating grid variables
2021-03-28 15:09:59,922:INFO:create_model_container: 0
2021-03-28 15:09:59,922:INFO:master_model_container: 0
2021-03-28 15:09:59,922:INFO:display_container: 1
2021-03-28 15:09:59,932:INFO:Pipeline(memory=None,
steps=[('dtypes',
DataTypes_Auto_infer(categorical_features=['BusinessTravel',
'Department',
'EducationField',
'JobRole', 'Gender',
'JobLevel',
'JobRole',
'MaritalStatus',
'OverTime',
'WorkLifeBalance'],
display_types=False,
features_todrop=['EmployeeNumber',
'StandardHours',
'EmployeeCount',
'Over18'],
id_columns=[],
ml_usecase='classification...
('P_transform', 'passthrough'), ('binn', 'passthrough'),
('rem_outliers', 'passthrough'), ('cluster_all', 'passthrough'),
('dummy', Dummify(target='Attrition')),
('fix_perfect', Remove_100(target='Attrition')),
('clean_names', Clean_Colum_Names()),
('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),
('dfs', 'passthrough'), ('pca', 'passthrough')],
verbose=False)
2021-03-28 15:09:59,932:INFO:setup() succesfully completed......................................
2021-03-28 15:10:00,031:INFO:Initializing create_model()
2021-03-28 15:10:00,031:INFO:create_model(kwargs={}, display=None, metrics=None, system=True, verbose=True, refit=True, groups=None, fit_kwargs=None, predict=True, cross_validation=True, round=4, fold=None, estimator=catboost)
2021-03-28 15:10:00,032:INFO:Checking exceptions
2021-03-28 15:20:23,015:INFO:PyCaret Supervised Module
2021-03-28 15:20:23,015:INFO:ML Usecase: classification
2021-03-28 15:20:23,015:INFO:version 2.3.0
2021-03-28 15:20:23,015:INFO:Initializing setup()
2021-03-28 15:20:23,015:INFO:setup(display=None, profile_kwargs=None, profile=False, verbose=False, silent=True, log_data=False, log_profile=False, log_plots=False, experiment_name=None, log_experiment=False, session_id=None, html=True, custom_pipeline=None, use_gpu=False, n_jobs=-1, fold_groups=None, fold_shuffle=False, fold=10, fold_strategy=kfold, data_split_stratify=False, data_split_shuffle=True, transform_target_method=box-cox, transform_target=False, fix_imbalance_method=None, fix_imbalance=False, interaction_threshold=0.01, feature_ratio=False, feature_interaction=False, feature_selection_method=classic, feature_selection_threshold=0.8, feature_selection=False, group_names=None, group_features=None, polynomial_threshold=0.1, trigonometry_features=False, polynomial_degree=2, polynomial_features=False, cluster_iter=20, create_clusters=False, remove_perfect_collinearity=True, multicollinearity_threshold=0.9, remove_multicollinearity=False, outliers_threshold=0.05, remove_outliers=False, bin_numeric_features=None, rare_level_threshold=0.1, combine_rare_levels=False, ignore_low_variance=False, pca_components=None, pca_method=linear, pca=False, unknown_categorical_method=least_frequent, handle_unknown_categorical=True, transformation_method=yeo-johnson, transformation=False, normalize_method=zscore, normalize=True, ignore_features=['EmployeeNumber', 'StandardHours', 'EmployeeCount', 'Over18'], date_features=None, numeric_iterative_imputer=lightgbm, numeric_imputation=mean, numeric_features=['DistanceFromHome', 'HourlyRate', 'DailyRate', 'MonthlyIncome', 'MonthlyRate', 'NumCompaniesWorked', 'PercentSalaryHike', 'TotalWorkingYears', 'YearsAtCompany', 'YearsInCurrentRole', 'YearsWithCurrManager', 'TrainingTimesLastYear', 'YearsSinceLastPromotion'], high_cardinality_method=frequency, high_cardinality_features=None, ordinal_features={'StockOptionLevel': ['0', '1', '2', '3'], 'EnvironmentSatisfaction': ['1', '2', '3', '4'], 'JobInvolvement': ['1', '2', '3', '4'], 'JobSatisfaction': ['1', '2', '3', '4'], 'Education': ['1', '2', '3', '4', '5'], 'PerformanceRating': ['3', '4'], 'RelationshipSatisfaction': ['1', '2', '3', '4'], 'WorkLifeBalance': ['1', '2', '3', '4']}, categorical_iterative_imputer=lightgbm, categorical_imputation=constant, categorical_features=['BusinessTravel', 'Department', 'EducationField', 'JobRole', 'Gender', 'JobLevel', 'JobRole', 'MaritalStatus', 'OverTime', 'WorkLifeBalance'], iterative_imputation_iters=5, imputation_type=simple, preprocess=True, test_data= Age BusinessTravel DailyRate Department \
1041 28 Travel_Rarely 866 Sales
184 53 Travel_Rarely 1084 Research & Development
1222 24 Travel_Rarely 240 Human Resources
67 45 Travel_Rarely 1339 Research & Development
220 36 Travel_Rarely 1396 Research & Development
... ... ... ... ...
1009 58 Travel_Rarely 1055 Research & Development
757 34 Travel_Rarely 216 Sales
1361 26 Travel_Frequently 1096 Research & Development
376 51 Travel_Rarely 1178 Sales
314 39 Travel_Rarely 117 Research & Development
DistanceFromHome Education EducationField EmployeeCount \
1041 5 3 Medical 1
184 13 2 Medical 1
1222 22 1 Human Resources 1
67 7 3 Life Sciences 1
220 5 2 Life Sciences 1
... ... ... ... ...
1009 1 3 Medical 1
757 1 4 Marketing 1
1361 6 3 Other 1
376 14 2 Life Sciences 1
314 10 1 Medical 1
EmployeeNumber EnvironmentSatisfaction Gender HourlyRate \
1041 1469 4 Male 84
184 250 4 Female 57
1222 1714 4 Male 58
67 86 2 Male 59
220 304 4 Male 62
... ... ... ... ...
1009 1423 4 Female 76
757 1047 2 Male 75
1361 1918 3 Male 61
376 500 3 Female 87
314 429 3 Male 99
JobInvolvement JobLevel JobRole JobSatisfaction \
1041 3 2 Sales Executive 1
184 4 2 Manufacturing Director 1
1222 1 1 Human Resources 3
67 3 3 Research Scientist 1
220 3 2 Laboratory Technician 2
... ... ... ... ...
1009 3 5 Research Director 1
757 4 2 Sales Executive 4
1361 4 1 Laboratory Technician 4
376 3 2 Sales Executive 4
314 3 4 Manager 1
MaritalStatus MonthlyIncome MonthlyRate NumCompaniesWorked Over18 \
1041 Single 8463 23490 0 Y
184 Divorced 4450 26250 1 Y
1222 Married 1555 11585 1 Y
67 Divorced 9724 18787 2 Y
220 Single 5914 9945 8 Y
... ... ... ... ... ...
1009 Married 19701 22456 3 Y
757 Divorced 9725 12278 0 Y
1361 Married 2544 7102 0 Y
376 Married 4936 14862 4 Y
314 Married 17068 5355 1 Y
OverTime PercentSalaryHike PerformanceRating RelationshipSatisfaction \
1041 No 18 3 4
184 No 11 3 3
1222 No 11 3 3
67 No 17 3 3
220 No 16 3 4